Dr An Tang, M.D., M.Sc.

Professeur adjoint;
Radiologie, Faculté de médecine;
Université de Montréal;
CHUM, CRCHUM et Institut du cancer de Montréal.

 

Mots clés : Cancer du foie, carcinome hépatocellulaire, métastases hépatiques, imagerie, imagerie par résonance magnétique, échographie, élastographie, biomarqueurs quantitatifs, stéatose, fibrose

Contact :

an.tang@umontreal.ca

Bureau: 514-890-8000 x.31418

2000 MD., Université de Sherbrooke

2005 Diplôme, Université de Montréal, Radiologie

2006 Résident, University of Toronto, Abdominal Imaging Fellowship, Laboratoire du Dr. Stephanie Wilson

2012 M.Sc., Université de Montréal, Laboratoire du Dr. Gilles Soulez

2012 Résident, University of California, San Diego, Laboratoire Dr. Claude Sirlin

2013-2017 FRQS Chercheur boursier : Junior 1                  
2011-2012 Bourse de fellowship, programme Fulbright
2011-2012 IRSC Bourse de fellowship

  • 2020-2022 (Co-PI) Operating Grant, Onco-Tech (Consortium composed of Oncopole, Medteq, Institut TransMedTech, Société de recherche sur le cancer)

    "Added value of shear wave viscoelasticity imaging, homodyned K tissue imaging and acoustic attenuation to assess liver cancer at ultrasound: A multiparametric machine learning approach" 
  • 2020-2022 (PI) Operating Grant

    Institut de valorisation des données (IVADO)
    "Ultrasound classification of chronic liver disease with deep learning"

  • 2018-2023 (PI) Operating Grant

    Canadian Institutes of Health Research (CIHR)
    "Quantitative ultrasound techniques for diagnosis of nonalcoholic steatohepatitis"

  • 2015-2018 (Co-PI) Operating Grant, Consortium for research and innovation in medical technologies in Quebec (MEDTEQ)

    "Cancer Analysis with DEep Learning Artifical intelligence (CANDELA)"
  • 2013-2015 (PI) Operating Grant, IRSC.

    "Comparison of MR and US Elastography with Liver Biopsy for Noninvasive Staging of Liver Fibrosis."

  • 2011-2012 (PI) Operating Grant. RSNA R&E Foundation, CHAR-GE Development Awards and Diabète Québec.

    "Randomized trial of liraglutide and insulin on hepatic steatosis."

 

PRIX ET RÉCOMPENSES

2015 Prix du jeune investigateur, Association Canadienne des radiologues.

2012 Prix Bernadette-Nogrady, Société Canadienne-Française de Radiologie.

2012 Médaille de Bronze, European Society of Gastrointestinal and Abdominal Radiology.

Description des intérêts de recherche :

  1. Modalités: imagerie par résonance magnétique (IRM), tomodensitométrie (CT), échographie (US).

  2. DWI-IVIM pour l’évaluation de l’inflammation hépatique, MRI-PDFF pour la quantification de la grasse hépatique, R2* pour la quantification du fer hépatique, élastographie par résonance magnétique pour la quantification de la fibrose hépatique.

  3. Apprentissage automatique (« machine learning ») pour la classification des tissus hépatiques et des tumeurs.

 

Projets en cours (techniques utilisées) :

  1. Modalités: imagerie par résonance magnétique (IRM), tomodensitométrie (CT), échographie (US).

  2. IRM de perfusion pour évaluer vascularisation tumorale, quantification de graisse par IRM, élastographie pour quantification de fibrose hépatique, T2* et R2* pour quantification de fer hépatique. 

 

Sites connexes : 

  1. Page CRCHUM

  2. Page NCBI

     

Plateformes : 

  1. Imagerie hépatique (cancer du foie et pathologies diffuses du foie)

  1. Catherine Huet, assistante de recherche, catherine.huet.chum@ssss.gouv.qc.ca

  2. Emmanuel Montagnon, associé de recherche, emmanuelmontagnon@gmail.com

  3. Jennifer Satterthwaite, assistante de recherche, Jennifer.satterthwaite.chum@ssss.gouv.qc.ca

  4. Thierry Lefebvre, MSc, thierry.lefebvre@live.ca

 

 

  • Tang, A., I. Cruite, and C.B. Sirlin, Toward a standardized system for hepatocellular carcinoma diagnosis using computed tomography and MRI. Expert Rev Gastroenterol Hepatol, 2013. 7(3): p. 269-79.

  • Tang, A., et al., Optimal Pancreatic Phase Delay with 64-Detector CT Scanner and Bolus-tracking Technique. Acad Radiol, 2014. 21(8): p. 977-85.

  • Tang, A., M.A. Valasek, and C.B. Sirlin, Update on the Liver Imaging Reporting and Data System: What the Pathologist Needs to Know. Adv Anat Pathol, 2015. 22(5): p. 314-22.

  • Tang, A., et al., Ultrasound Elastography and MR Elastography for Assessing Liver Fibrosis: Part 2, Diagnostic Performance, Confounders, and Future Directions. AJR Am J Roentgenol, 2015. 205(1): p. 33-40.

  • Tang, A., et al., Ultrasound Elastography and MR Elastography for Assessing Liver Fibrosis: Part 1, Principles and Techniques. AJR Am J Roentgenol, 2015. 205(1): p. 22-32.

  • Costa, E.A., et al., Diagnostic Accuracy of Preoperative Gadoxetic Acid-enhanced 3-T MR Imaging for Malignant Liver Lesions by Using Ex Vivo MR Imaging-matched Pathologic Findings as the Reference Standard. Radiology, 2015. 276(3): p. 775-86.

  • Cruite, I., A. Tang, and C.B. Sirlin, Imaging-based diagnostic systems for hepatocellular carcinoma. AJR Am J Roentgenol, 2013. 201(1): p. 41-55.

  • Grasland-Mongrain, P., et al., Contactless remote induction of shear waves in soft tissues using a transcranial magnetic stimulation device. Phys Med Biol, 2016. 61(6): p. 2582-93.

  • Hanna, R.F., V.Z. Miloushev, and A. Tang, Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. 2016. 41(1): p. 71-90.

  • Kadoury, S., E. Vorontsov, and A. Tang, Metastatic liver tumour segmentation from discriminant Grassmannian manifolds. Phys Med Biol, 2015. 60(16): p. 6459-78.

  • Marks, R.M., et al., Diagnostic per-patient accuracy of an abbreviated hepatobiliary phase gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance. AJR Am J Roentgenol, 2015. 204(3): p. 527-35.

  • Santillan, C.S., et al., Understanding LI-RADS: a primer for practical use. Magn Reson Imaging Clin N Am, 2014. 22(3): p. 337-52.

  • Shah, A., et al., Cirrhotic liver: What's that nodule? The LI-RADS approach. J Magn Reson Imaging, 2016. 43(2): p. 281-94.

  • Tang A, Abukasm K, Cunha GM, Song B, Wang J, Wai A, Wagner M, Dietrich C, Brancatelli G, Ueda K, Choi JY, Aguirre D, Sirlin CB. Imaging of hepatocellular carcinoma: a pilot international survey [published online ahead of print, 2020 Jun 1]. Abdom Radiol (NY). 2020;10.1007/s00261-020-02598-0. doi:10.1007/s00261-020-02598-0 DOI: 10.1007/s00261-020-02598-0. https://rdcu.be/b4zYP
     
  • Mansour R, Thibodeau Antonacci A, Bilodeau L, Romaguera L, Cerny M, Gilbert G, Tang A, Kadoury S. Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence. Physics in Medicine and Biology. 2020;65(8):085004. Published 2020 Apr 17. doi:10.1088/1361-6560/ab78be
     
  • Maaref A, Perdigon Romero F, Montagnon E, Cerny M, Nguyen B, Vandenbroucke-Menu F, Geneviève S, Turcotte S, Tang A, Kadoury S. Predicting the Response to FOLFOX-based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: A Deep Neural Network Approach. Journal of Digital Imaging. 2020 Mar 19. doi: 10.1007/s10278-020-00332-2. PubMed PMID: 32193665. https://link.springer.com/article/10.1007/s10278-020-00332-2

  •  Voizard N, Cerny M, Assad A, Billiard JS, Olivié D, Perreault P, Kielar A, Do RKG, Yokoo T, Sirlin CB, Tang A. Assessment of Hepatocellular Carcinoma Treatment Response with LI-RADS: a Pictorial Review. Insights into Imaging. 2019 Dec 18;10(1):121. doi: 10.1186/s13244-019-0801-z. Review. PMID: 31853668
    https://link.springer.com/article/10.1186/s13244-019-0801-z

  • Featured in: Highlights in Insights into Imaging
    http://www.myesr.link/Mailings/highlights-in-insights-into-imaging-january-2020-(new)/

  •  Thibodeau-Antonacci A, Petitclerc L, Gilbert G, Bilodeau L, Olivié D, Cerny M, Castel H, Turcotte S, Huet C, Perreault P, Soulez G, Chagnon M, Kadoury S, Tang A. Dynamic Contrast-Enhanced MRI to Assess Hepatocellular Carcinoma Response to Transarterial Chemoembolization Using LI-RADS Criteria: a Pilot Study. Magn Reson Imaging. 2019 Jun 25;62:78-86. doi: 10.1016/j.mri.2019.06.017. [Epub ahead of print] https://doi.org/10.1016/j.mri.2019.06.017